Efficient Numerical Wave Propagation Enhanced By An End-to-End Deep Learning Model
This addresses the need for efficient wave modeling in scientific and engineering domains, but is incremental as it builds directly on existing work.
The paper tackles the problem of efficient high-fidelity wave propagation by integrating a numerical solver with deep learning into an end-to-end framework, building on prior work. Results show the cohesive structure improves performance without sacrificing speed, demonstrating the importance of temporal dynamics and a parallel-in-time algorithm.
In a variety of scientific and engineering domains, the need for high-fidelity and efficient solutions for high-frequency wave propagation holds great significance. Recent advances in wave modeling use sufficiently accurate fine solver outputs to train a neural network that enhances the accuracy of a fast but inaccurate coarse solver. In this paper we build upon the work of Nguyen and Tsai (2023) and present a novel unified system that integrates a numerical solver with a deep learning component into an end-to-end framework. In the proposed setting, we investigate refinements to the network architecture and data generation algorithm. A stable and fast solver further allows the use of Parareal, a parallel-in-time algorithm to correct high-frequency wave components. Our results show that the cohesive structure improves performance without sacrificing speed, and demonstrate the importance of temporal dynamics, as well as Parareal, for accurate wave propagation.